Divergent Thinking Tools

A Paralogy Product

Everyone using AI is getting more or less the same output. This tool fixes that.

Divergent Thinking Tools is a pipeline of structured skills, each one designed to break a specific failure mode in AI reasoning, packaged with a router that knows which skill to deploy and when.

AI models are pattern-completion machines. Trained on the sum of human thought, they're also trained on the sum of human conventional thought. They converge. They average. They produce what's most probable, not what's most valuable.

Researchers at CHI 2025 call it "mechanised convergence." AI makes any one person's output better, but it makes everyone's output more similar. It nudges groups toward smaller, less diverse sets of ideas. A Royal Society study confirmed a persistent generalization bias in LLMs. Knowledge workers describe AI output as "too cookie cutter, full of cliche."

Quick Start · The Tools · For Agents · The Pipeline · Test Results · Before & After · Research · Limitations

Quick Start

Install via npx

No clone, no build, no API key. The server distributes skill prompts — your client's LLM executes them.

Claude Code:

claude mcp add divergent-thinking-tools -- npx -y divergent-thinking-tools-mcp

codex mcp add divergent-thinking-tools -- npx -y divergent-thinking-tools-mcp

Add to your config (~/Library/Application Support/Claude/claude_desktop_config.json or .cursor/mcp.json):

{
  "mcpServers": {
    "divergent-thinking-tools": {
      "command": "npx",
      "args": ["-y", "divergent-thinking-tools-mcp"]
    }
  }
}

Hosted server (no install at all)

If you'd rather not run anything locally, connect to the hosted server.

Claude.ai:

1. Go to claude.ai/customize/connectors
2. Click + → Name: Divergent Thinking Tools → URL: https://mcp.paralogy.ai/mcp
3. Start a new chat and describe your problem. The Router handles the rest.

claude mcp add divergent-thinking-tools --transport http https://mcp.paralogy.ai/mcp
codex mcp add divergent-thinking-tools --transport http https://mcp.paralogy.ai/mcp

{
  "mcpServers": {
    "divergent-thinking-tools": {
      "url": "https://mcp.paralogy.ai/mcp"
    }
  }
}

1. Go to Settings → Apps → Advanced settings → toggle Developer mode on
2. Click Create app → Name: Paralogy → URL: https://mcp.paralogy.ai/mcp → Authentication: No Auth
3. Check the acknowledgment box and click Create
4. Start a new chat and describe your problem. Reference tools explicitly for best results: "Use the Paralogy app's think-wrong tool to..."

See mcp-server/README.md for full ChatGPT setup details.

openclaw mcp set divergent-thinking-tools '{"url":"https://mcp.paralogy.ai/mcp"}'

All 15 tools become available to any OpenClaw agent. See docs/for-openclaw.md for dedicated agent and multi-agent pipeline setups.

1. Download the skills/ directory
2. Add the SKILL.md files to your Claude Projects, Cursor, or any LLM custom instructions
3. Describe your problem. The Router handles the rest.

See docs/for-humans.md for details.

The Tools

Exploration:

• Wrong Problem Detector — runs 5 checks on the problem before you solve it
• Dumb Questions Engine — generates 15-20 questions nobody is asking

Pre-generation:

• Strip Down — translates detailed briefs into raw human desire, so the AI generates from internal associations instead of rephrasing your words back at you
• Blind Spot Scan — maps the full problem space before and after ideation

Generation (8 tools, each structurally different):

• Guilford Engine — structured five-pass divergent thinking (fluency, flexibility, originality, feasibility, clarity)
• Persona Divergence Engine — ideas from 5 radically different constructed worldviews
• Think Wrong — single counterintuitive argument that makes experts defensive, not comfortable
• Short Think — 30+ gut-level ideas with zero deliberation
• Bad on Purpose — deliberately terrible ideas, then reverse-engineers the hidden value inside them
• Wild to Mild — ideas at 4 altitude levels from Monday Morning to Moonshot
• MacGyver Mode — solutions using only existing resources, no new budget, no new hires
• Random Injection — injects an unrelated concept and forces deep structural connections to the problem

Post-generation:

• Anti-Homogeneity Check — diversity audit with letter grades
• Blind Spot Scan (post) — coverage audit identifying empty zones

Polish:

• De-Slop — transforms AI-polished text into authentic human voice

For Agents

The tools map to five infrastructure layers inside agentic systems:

See docs/for-agents.md for the full agentic architecture, implementation patterns, and use case examples.

The Pipeline

   Explore        Pre-generate       Generate          Audit           Gap-fill          Polish
      │                │                │                │                │                │
      ▼                ▼                ▼                ▼                ▼                ▼
┌───────────┐    ┌───────────┐    ┌───────────┐    ┌───────────┐    ┌───────────┐    ┌───────────┐
│   Wrong   │    │ Strip Down│    │  Guilford │    │   Anti-   │    │  Router   │    │  De-Slop  │
│  Problem  │───▶│ + Blind   │───▶│ + Persona │───▶│ Homogene- │───▶│ targeted  │───▶│   human   │
│ Detector  │    │ Spot Scan │    │ + 6 more  │    │ ity Check │    │   regen   │    │   voice   │
└───────────┘    └───────────┘    └───────────┘    └───────────┘    └───────────┘    └───────────┘
  Check the       Extract raw      8 generators,    Diversity grade   Fill gaps,        Strip
  problem first   desire, map      each producing   + coverage audit  don't just        the AI
                  the full space   structurally                       point at them      polish
                                   different output

The Router orchestrates the pipeline. You don't manage tool selection — the Router reads the situation and acts.

Test Results

10 full pipeline tests across 10 domains:

Before & After

Same problem. Same model. Climate tech go-to-market strategy.

Vanilla Claude (no tools):

22 ideas — conventional channels, obvious partnerships,
standard go-to-market playbook. Solid but predictable.

With the pipeline:

65+ ideas — including 15 that vanilla Claude didn't touch:

  • FEMA grantees with approved funding and no vendor
  • DOD procurement programs nobody at the startup knew existed
  • Complete reframing: "we can't sell to utilities" →
    "we're selling to the wrong buyer"

Same model. Same problem. 3x the ideas. The difference is structural — the pipeline forces the model into territory its default reasoning actively avoids.

Full comparison: test-6-control-vanilla-vs-tools.md.

The Research

• Guilford, J.P. (1967). The Nature of Human Intelligence. The five-dimension divergent thinking framework.
• Ruan et al. (2026). LiveIdeaBench. Nature Communications. Validated the framework across 40+ LLMs and 22 scientific domains.
• Doshi & Hauser (2024). Science Advances. AI makes individuals more creative but makes everyone's output more similar.
• Wenger & Kenett (2025). LLMs show high creative homogeneity (0.92 cosine similarity) even across model families.
• Research on LLM creativity confirms a "sweet spot" where carefully calibrated prompts unlock richer associative thinking.

Limitations

• Tested across 10 domains including 3 technical (engineering, physics, applied math) — all scored A or A-. Strongest at structural and architectural reframing; weakest when the answer is a derivation or proof. The tools don't replace domain expertise, but they help experts decide where to point it.
• The tools grade their own output. Diversity scores are self-assessed, not externally validated.
• All tests ran on Claude. Other models may handle the complex multi-step instructions differently.
• Via MCP server: lightweight. Tool descriptions cost ~1,500 tokens total. Each skill prompt loads only when called. Via SKILL.md files: the full set is ~49,000 tokens of system prompt. The minimum viable set is ~18,000 tokens. See docs/for-humans.md for the recommended starting configuration.

Built by David Carson at Paralogy.